Cognitive network framework for heterogeneous wireless mesh systems

Heterogeneous wireless mesh networks (WMN) provide an opportunity to secure higher network capacity, wider coverage and higher quality of service (QoS). However, heterogeneous systems are complex to configure because of the high diversity of associated devices and resources. This thesis introduces a novel cognitive network framework that allows the integration of WMNs with long-term evolution (LTE) networks so that none of the overlapped frequency bands are used. The framework consists of three novel systems: the QoS metrics management system, the heterogeneous network management system and the routing decision-making system. The novelty of the QoS metrics management system is that it introduces a new routing metric for multi-hop wireless networks by developing a new rate adaptation algorithm. This system directly addresses the interference between neighbouring nodes, which has not been addressed in previous research on rate adaptation for WMN. The results indicated that there was a significant improvement in the system throughput by as much as to 90%. The routing decision-making system introduces two novel methods to select the transmission technology in heterogeneous nodes: the cognitive heterogeneous routing (CHR) system and the semantic reasoning system. The CHR method is used to develop a novel reinforcement learning algorithm to optimise the selection of transmission technology on wireless heterogeneous nodes by learning from previous actions. The semantic reasoning method uses ontologies and fuzzy-based semantic reasoning to facilitate the dynamic addition of new network types to the heterogeneous network. The simulation results showed that the heterogeneous network outperformed the benchmark networks by up to 200% of the network throughput

EdgeRIC: Empowering Realtime Intelligent Optimization and Control in NextG Networks

Radio Access Networks (RAN) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we develop a RIC platform called RICworld, consisting of (i) EdgeRIC, which is colocated, but decoupled from the RAN stack, and can access RAN and application-level information to execute AI-optimized and other policies in realtime (sub-millisecond) and (ii) DigitalTwin, a full-stack, trace-driven emulator for training AI-based policies offline. We demonstrate that realtime EdgeRIC operates as if embedded within the RAN stack and significantly outperforms a cloud-based near-realtime RIC (> 15 ms latency) in terms of attained throughput. We train AI-based polices on DigitalTwin, execute them on EdgeRIC, and show that these policies are robust to channel dynamics, and outperform queueing-model based policies by 5% to 25% on throughput and application-level benchmarks in a variety of mobile environments.Comment: 16 pages, 15 figure

Mobile and Wireless Communications

Mobile and Wireless Communications have been one of the major revolutions of the late twentieth century. We are witnessing a very fast growth in these technologies where mobile and wireless communications have become so ubiquitous in our society and indispensable for our daily lives. The relentless demand for higher data rates with better quality of services to comply with state-of-the art applications has revolutionized the wireless communication field and led to the emergence of new technologies such as Bluetooth, WiFi, Wimax, Ultra wideband, OFDMA. Moreover, the market tendency confirms that this revolution is not ready to stop in the foreseen future. Mobile and wireless communications applications cover diverse areas including entertainment, industrialist, biomedical, medicine, safety and security, and others, which definitely are improving our daily life. Wireless communication network is a multidisciplinary field addressing different aspects raging from theoretical analysis, system architecture design, and hardware and software implementations. While different new applications are requiring higher data rates and better quality of service and prolonging the mobile battery life, new development and advanced research studies and systems and circuits designs are necessary to keep pace with the market requirements. This book covers the most advanced research and development topics in mobile and wireless communication networks. It is divided into two parts with a total of thirty-four stand-alone chapters covering various areas of wireless communications of special topics including: physical layer and network layer, access methods and scheduling, techniques and technologies, antenna and amplifier design, integrated circuit design, applications and systems. These chapters present advanced novel and cutting-edge results and development related to wireless communication offering the readers the opportunity to enrich their knowledge in specific topics as well as to explore the whole field of rapidly emerging mobile and wireless networks. We hope that this book will be useful for students, researchers and practitioners in their research studies

Aspects of knowledge mining on minimizing drive tests in self-organizing cellular networks

The demand for mobile data traffic is about to explode and this drives operators to find ways to further increase the offered capacity in their networks. If networks are deployed in the traditional way, this traffic explosion will be addressed by increasing the number of network elements significantly. This is expected to increase the costs and the complexity of planning, operating and optimizing the networks. To ensure effective and cost-efficient operations, a higher degree of automation and self-organization is needed in the next generation networks. For this reason, the concept of self-organizing networks was introduced in LTE covering multitude of use cases. This was specifically done in the areas of self-configuration, self-optimization and selfhealing of networks. From an operator’s perspective, automated collection and analysis of field measurements while complementing the traditional drive test campaigns is one of the top use cases that can provide significant cost savings in self-organizing networks. This thesis studies the Minimization of Drive Tests in self-organizing cellular networks from three different aspects. The first aspect is network operations, and particularly the network fault management process, as the traditional drive tests are often conducted for troubleshooting purposes. The second aspect is network functionality, and particularly the technical details about the specified measurement and signaling procedures in different network elements that are needed for automating the collection of the field measurement data. The third aspect concerns the analysis of the measurement databases that is a process used for increasing the degree of automation and self-awareness in the networks, and particularly the mathematical means for autonomously finding meaningful patterns of knowledge from huge amounts of data. Although the above mentioned technical areas have been widely discussed in previous literature, it has been done separately and only a few papers discuss how for example, knowledge mining is employed for processing field measurement data in a way that minimizes the drive tests in self-organizing LTE networks. The objective of the thesis is to use well known knowledge mining principles to develop novel self-healing and self-optimization algorithms. These algorithms analyze MDT databases to detect coverage holes, sleeping cells and other geographical areas of anomalous network behavior. The results of the research suggest that by employing knowledge mining in processing the MDT databases, one can acquire knowledge for discriminating between different network problems and detecting anomalous network behavior. For example, downlink coverage optimization is enhanced by classifying RLF reports into coverage, interference and handover problems. Moreover, by incorporating a normalized power headroom report with the MDT reports, better discrimination between uplink coverage problems and the parameterization problems is obtained. Knowledge mining is also used to detect sleeping cells by means of supervised and unsupervised learning. The detection framework is based on a novel approach where diffusion mapping is used to learn about network behavior in its healthy state. The sleeping cells are detected by observing an increase in the number of anomalous reports associated with a certain cell. The association is formed by correlating the geographical location of anomalous reports with the estimated dominance areas of the cells. Moreover, RF fingerprint positioning of the MDT reports is studied and the results suggest that RF fingerprinting can provide a quite detailed location estimation in dense heterogeneous networks. In addition, self-optimization of the mobility state estimation parameters is studied in heterogeneous LTE networks and the results suggest that by gathering MDT measurements and constructing statistical velocity profiles, MSE parameters can be adjusted autonomously, thus resulting in reasonably good classification accuracy. The overall outcome of the thesis is as follows. By automating the classification of the measurement reports between certain problems, network engineers can acquire knowledge about the root causes of the performance degradation in the networks. This saves time and resources and results in a faster decision making process. Due to the faster decision making process the duration of network breaks become shorter and the quality of the network is improved. By taking into account the geographical locations of the anomalous field measurements in the network performance analysis, finer granularity for estimating the location of the problem areas can be achieved. This can further improve the operational decision making that guides the corresponding actions for example, where to start the network optimization. Moreover, by automating the time and resource consuming task of tuning the mobility state estimation parameters, operators can enhance the mobility performance of the high velocity UEs in heterogeneous radio networks in a cost-efficient and backward compatible manner

A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

5G and beyond networks

This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications

Benefits and limits of machine learning for the implicit coordination on SON functions

Bedingt durch die Einführung neuer Netzfunktionen in den Mobilfunknetzen der nächsten Generation, z. B. Slicing oder Mehrantennensysteme, sowie durch die Koexistenz mehrerer Funkzugangstechnologien, werden die Optimierungsaufgaben äußerst komplex und erhöhen die OPEX (OPerational EXpenditures). Um den Nutzern Dienste mit wettbewerbsfähiger Dienstgüte (QoS) zu bieten und gleichzeitig die Betriebskosten niedrig zu halten, wurde von den Standardisierungsgremien das Konzept des selbstorganisierenden Netzes (SON) eingeführt, um das Netzmanagement um eine Automatisierungsebene zu erweitern. Es wurden dafür mehrere SON-Funktionen (SFs) vorgeschlagen, um einen bestimmten Netzbereich, wie Abdeckung oder Kapazität, zu optimieren. Bei dem konventionellen Entwurf der SFs wurde jede Funktion als Regler mit geschlossenem Regelkreis konzipiert, der ein lokales Ziel durch die Einstellung bestimmter Netzwerkparameter optimiert. Die Beziehung zwischen mehreren SFs wurde dabei jedoch bis zu einem gewissen Grad vernachlässigt. Daher treten viele widersprüchliche Szenarien auf, wenn mehrere SFs in einem mobilen Netzwerk instanziiert werden. Solche widersprüchlichen Funktionen in den Netzen verschlechtern die QoS der Benutzer und beeinträchtigen die Signalisierungsressourcen im Netz. Es wird daher erwartet, dass eine existierende Koordinierungsschicht (die auch eine Entität im Netz sein könnte) die Konflikte zwischen SFs lösen kann. Da diese Funktionen jedoch eng miteinander verknüpft sind, ist es schwierig, ihre Interaktionen und Abhängigkeiten in einer abgeschlossenen Form zu modellieren. Daher wird maschinelles Lernen vorgeschlagen, um eine gemeinsame Optimierung eines globalen Leistungsindikators (Key Performance Indicator, KPI) so voranzubringen, dass die komplizierten Beziehungen zwischen den Funktionen verborgen bleiben. Wir nennen diesen Ansatz: implizite Koordination. Im ersten Teil dieser Arbeit schlagen wir eine zentralisierte, implizite und auf maschinellem Lernen basierende Koordination vor und wenden sie auf die Koordination zweier etablierter SFs an: Mobility Robustness Optimization (MRO) und Mobility Load Balancing (MLB). Anschließend gestalten wir die Lösung dateneffizienter (d. h. wir erreichen die gleiche Modellleistung mit weniger Trainingsdaten), indem wir eine geschlossene Modellierung einbetten, um einen Teil des optimalen Parametersatzes zu finden. Wir nennen dies einen "hybriden Ansatz". Mit dem hybriden Ansatz untersuchen wir den Konflikt zwischen MLB und Coverage and Capacity Optimization (CCO) Funktionen. Dann wenden wir ihn auf die Koordinierung zwischen MLB, Inter-Cell Interference Coordination (ICIC) und Energy Savings (ES) Funktionen an. Schließlich stellen wir eine Möglichkeit vor, MRO formal in den hybriden Ansatz einzubeziehen, und zeigen, wie der Rahmen erweitert werden kann, um anspruchsvolle Netzwerkszenarien wie Ultra-Reliable Low Latency Communications (URLLC) abzudecken.Due to the introduction of new network functionalities in next-generation mobile networks, e.g., slicing or multi-antenna systems, as well as the coexistence of multiple radio access technologies, the optimization tasks become extremely complex, increasing the OPEX (OPerational EXpenditures). In order to provide services to the users with competitive Quality of Service (QoS) while keeping low operational costs, the Self-Organizing Network (SON) concept was introduced by the standardization bodies to add an automation layer to the network management. Thus, multiple SON functions (SFs) were proposed to optimize a specific network domain, like coverage or capacity. The conventional design of SFs conceived each function as a closed-loop controller optimizing a local objective by tuning specific network parameters. However, the relationship among multiple SFs was neglected to some extent. Therefore, many conflicting scenarios appear when multiple SFs are instantiated in a mobile network. Having conflicting functions in the networks deteriorates the users’ QoS and affects the signaling resources in the network. Thus, it is expected to have a coordination layer (which could also be an entity in the network), conciliating the conflicts between SFs. Nevertheless, due to interleaved linkage among those functions, it is complex to model their interactions and dependencies in a closed form. Thus, machine learning is proposed to drive a joint optimization of a global Key Performance Indicator (KPI), hiding the intricate relationships between functions. We call this approach: implicit coordination. In the first part of this thesis, we propose a centralized, fully-implicit coordination approach based on machine learning (ML), and apply it to the coordination of two well-established SFs: Mobility Robustness Optimization (MRO) and Mobility Load Balancing (MLB). We find that this approach can be applied as long as the coordination problem is decomposed into three functional planes: controllable, environmental, and utility planes. However, the fully-implicit coordination comes at a high cost: it requires a large amount of data to train the ML models. To improve the data efficiency of our approach (i.e., achieving good model performance with less training data), we propose a hybrid approach, which mixes ML with closed-form models. With the hybrid approach, we study the conflict between MLB and Coverage and Capacity Optimization (CCO) functions. Then, we apply it to the coordination among MLB, Inter-Cell Interference Coordination (ICIC), and Energy Savings (ES) functions. With the hybrid approach, we find in one shot, part of the parameter set in an optimal manner, which makes it suitable for dynamic scenarios in which fast response is expected from a centralized coordinator. Finally, we present a manner to formally include MRO in the hybrid approach and show how the framework can be extended to cover challenging network scenarios like Ultra-Reliable Low Latency Communications (URLLC)